






"""simple docstring"""
import unittest

import numpy as np

from transformers import RobertaPreLayerNormConfig, is_flax_available
from transformers.testing_utils import require_flax, slow

from ...test_modeling_flax_common import FlaxModelTesterMixin, floats_tensor, ids_tensor, random_attention_mask


if is_flax_available():
 import jax.numpy as jnp

 from transformers.models.roberta_prelayernorm.modeling_flax_roberta_prelayernorm import (
     FlaxRobertaPreLayerNormForCausalLM,
     FlaxRobertaPreLayerNormForMaskedLM,
     FlaxRobertaPreLayerNormForMultipleChoice,
     FlaxRobertaPreLayerNormForQuestionAnswering,
     FlaxRobertaPreLayerNormForSequenceClassification,
     FlaxRobertaPreLayerNormForTokenClassification,
     FlaxRobertaPreLayerNormModel,
 )



class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    def __init__(		self :	Optional[Any]				,    UpperCamelCase_ :	Optional[int]				,    UpperCamelCase_ :	Dict=1_3				,    UpperCamelCase_ :	Optional[int]=7				,    UpperCamelCase_ :	Any=True				,    UpperCamelCase_ :	Optional[int]=True				,    UpperCamelCase_ :	str=True				,    UpperCamelCase_ :	int=True				,    UpperCamelCase_ :	int=9_9				,    UpperCamelCase_ :	Tuple=3_2				,    UpperCamelCase_ :	Tuple=5				,    UpperCamelCase_ :	Tuple=4				,    UpperCamelCase_ :	List[str]=3_7				,    UpperCamelCase_ :	Union[str, Any]="gelu"				,    UpperCamelCase_ :	Dict=0.1				,    UpperCamelCase_ :	List[str]=0.1				,    UpperCamelCase_ :	str=5_1_2				,    UpperCamelCase_ :	List[Any]=1_6				,    UpperCamelCase_ :	int=2				,    UpperCamelCase_ :	List[Any]=0.02				,    UpperCamelCase_ :	Optional[int]=4				,    ):





           '''simple docstring'''

           __magic_name__      				=    parent
           __magic_name__      				=    batch_size
           __magic_name__      				=    seq_length
           __magic_name__      				=    is_training
           __magic_name__      				=    use_attention_mask
           __magic_name__      				=    use_token_type_ids
           __magic_name__      				=    use_labels
           __magic_name__      				=    vocab_size
           __magic_name__      				=    hidden_size
           __magic_name__      				=    num_hidden_layers
           __magic_name__      				=    num_attention_heads
           __magic_name__      				=    intermediate_size
           __magic_name__      				=    hidden_act
           __magic_name__      				=    hidden_dropout_prob
           __magic_name__      				=    attention_probs_dropout_prob
           __magic_name__      				=    max_position_embeddings
           __magic_name__      				=    type_vocab_size
           __magic_name__      				=    type_sequence_label_size
           __magic_name__      				=    initializer_range
           __magic_name__      				=    num_choices







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    self.vocab_size )

           __magic_name__      				=    None
           if self.use_attention_mask:
                  __magic_name__      				=    random_attention_mask([self.batch_size, self.seq_length] )

           __magic_name__      				=    None
           if self.use_token_type_ids:
                  __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    self.type_vocab_size )

           __magic_name__      				=    RobertaPreLayerNormConfig(
               vocab_size=self.vocab_size				,    hidden_size=self.hidden_size				,    num_hidden_layers=self.num_hidden_layers				,    num_attention_heads=self.num_attention_heads				,    intermediate_size=self.intermediate_size				,    hidden_act=self.hidden_act				,    hidden_dropout_prob=self.hidden_dropout_prob				,    attention_probs_dropout_prob=self.attention_probs_dropout_prob				,    max_position_embeddings=self.max_position_embeddings				,    type_vocab_size=self.type_vocab_size				,    is_decoder=UpperCamelCase_				,    initializer_range=self.initializer_range				,    )

           return config, input_ids, token_type_ids, attention_mask







    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    self.prepare_config_and_inputs()
           __magic_name__				,  __magic_name__				,  __magic_name__				,  __magic_name__      				=    config_and_inputs
           __magic_name__      				=    {'input_ids': input_ids, 'token_type_ids': token_type_ids, 'attention_mask': attention_mask}
           return config, inputs_dict







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    self.prepare_config_and_inputs()
           __magic_name__				,  __magic_name__				,  __magic_name__				,  __magic_name__      				=    config_and_inputs

           __magic_name__      				=    True
           __magic_name__      				=    floats_tensor([self.batch_size, self.seq_length, self.hidden_size] )
           __magic_name__      				=    ids_tensor([self.batch_size, self.seq_length]				,    vocab_size=2 )

           return (
               config,
               input_ids,
               token_type_ids,
               encoder_hidden_states,
               encoder_attention_mask,
           )



@require_flax
# Copied from tests.models.roberta.test_modelling_flax_roberta.FlaxRobertaPreLayerNormModelTest with ROBERTA->ROBERTA_PRELAYERNORM,Roberta->RobertaPreLayerNorm,roberta-base->andreasmadsen/efficient_mlm_m0.40
class        UpperCamelCase__ (				a_							,	unittest.TestCase):







    """simple docstring"""
    __UpperCAmelCase           = True

    __UpperCAmelCase           = (
        (
            FlaxRobertaPreLayerNormModel,
            FlaxRobertaPreLayerNormForCausalLM,
            FlaxRobertaPreLayerNormForMaskedLM,
            FlaxRobertaPreLayerNormForSequenceClassification,
            FlaxRobertaPreLayerNormForTokenClassification,
            FlaxRobertaPreLayerNormForMultipleChoice,
            FlaxRobertaPreLayerNormForQuestionAnswering,
        )
        if is_flax_available()
        else ()
    )







    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    FlaxRobertaPreLayerNormModelTester(self )







    @slow
    def 				a__			(		self :	Union[str, Any] ):





           '''simple docstring'''

           for model_class_name in self.all_model_classes:
                  __magic_name__      				=    model_class_name.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,    from_pt=UpperCamelCase_ )
                  __magic_name__      				=    model(np.ones((1, 1) ) )
                  self.assertIsNotNone(UpperCamelCase_ )



@require_flax
class        UpperCamelCase__ (				unittest.TestCase):







    """simple docstring"""







    @slow
    def 				a__			(		self :	Any ):





           '''simple docstring'''

           __magic_name__      				=    FlaxRobertaPreLayerNormForMaskedLM.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,    from_pt=UpperCamelCase_ )

           __magic_name__      				=    np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				,    dtype=jnp.intaa )
           __magic_name__      				=    model(UpperCamelCase_ )[0]
           __magic_name__      				=    [1, 1_1, 5_0_2_6_5]
           self.assertEqual(list(output.shape )				,    UpperCamelCase_ )
           # compare the actual values for a slice.
           __magic_name__      				=    np.array(
               [[[40.4_880, 18.0_199, -5.2_367], [-1.8_877, -4.0_885, 10.7_085], [-2.2_613, -5.6_110, 7.2_665]]]				,    dtype=np.floataa )
           self.assertTrue(np.allclose(output[:, :3, :3]				,    UpperCamelCase_				,    atol=1e-4 ) )







    @slow
    def 				a__			(		self :	Optional[int] ):





           '''simple docstring'''

           __magic_name__      				=    FlaxRobertaPreLayerNormModel.from_pretrained('andreasmadsen/efficient_mlm_m0.40'				,    from_pt=UpperCamelCase_ )

           __magic_name__      				=    np.array([[0, 3_1_4_1_4, 2_3_2, 3_2_8, 7_4_0, 1_1_4_0, 1_2_6_9_5, 6_9, 4_6_0_7_8, 1_5_8_8, 2]]				,    dtype=jnp.intaa )
           __magic_name__      				=    model(UpperCamelCase_ )[0]
           # compare the actual values for a slice.
           __magic_name__      				=    np.array(
               [[[0.0_208, -0.0_356, 0.0_237], [-0.1_569, -0.0_411, -0.2_626], [0.1_879, 0.0_125, -0.0_089]]]				,    dtype=np.floataa )
           self.assertTrue(np.allclose(output[:, :3, :3]				,    UpperCamelCase_				,    atol=1e-4 ) )







"""simple docstring"""
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional

import evaluate
import numpy as np
import torch
from datasets import load_dataset
from PIL import Image
from torchvision.transforms import (
    CenterCrop,
    Compose,
    Normalize,
    RandomHorizontalFlip,
    RandomResizedCrop,
    Resize,
    ToTensor,
)

import transformers
from transformers import (
    MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING,
    AutoConfig,
    AutoImageProcessor,
    AutoModelForImageClassification,
    HfArgumentParser,
    Trainer,
    TrainingArguments,
    set_seed,
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version, send_example_telemetry
from transformers.utils.versions import require_version
snake_case : Tuple     		=		logging.getLogger(__name__)

# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("""4.31.0""")

require_version("""datasets>=1.8.0""", """To fix: pip install -r examples/pytorch/image-classification/requirements.txt""")

snake_case : List[str]     		=		list(MODEL_FOR_IMAGE_CLASSIFICATION_MAPPING.keys())
snake_case : int     		=		tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)


def 							A						(				__snake_case: str  )    ->		Tuple:

       """simple docstring"""





       with open(__snake_case ,     'rb'  ) as f:
              __magic_name__      				=    Image.open(__snake_case  )
              return im.convert('RGB'  )



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": """Name of a dataset from the hub (could be your own, possibly private dataset hosted on the hub)."""
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """The configuration name of the dataset to use (via the datasets library)."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """A folder containing the training data."""})
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """A folder containing the validation data."""})
    __UpperCAmelCase           = field(
        default=0.15							,	metadata={"""help""": """Percent to split off of train for validation."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of training examples to this """
                """value if set."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """For debugging purposes or quicker training, truncate the number of evaluation examples to this """
                """value if set."""
            )
        }							,	)







    def 				a__			(		self :	Tuple ):





           '''simple docstring'''

           if self.dataset_name is None and (self.train_dir is None and self.validation_dir is None):
                  raise ValueError(
                      'You must specify either a dataset name from the hub or a train and/or validation directory.' )



@dataclass
class        UpperCamelCase__ :







    """simple docstring"""
    __UpperCAmelCase           = field(
        default="""google/vit-base-patch16-224-in21k"""							,	metadata={"""help""": """Path to pretrained model or model identifier from huggingface.co/models"""}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """If training from scratch, pass a model type from the list: """ + """, """.join(a_)}							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Pretrained config name or path if not the same as model_name"""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Where do you want to store the pretrained models downloaded from s3"""})
    __UpperCAmelCase           = field(
        default="""main"""							,	metadata={"""help""": """The specific model version to use (can be a branch name, tag name or commit id)."""}							,	)
    __UpperCAmelCase           = field(default=a_							,	metadata={"""help""": """Name or path of preprocessor config."""})
    __UpperCAmelCase           = field(
        default=a_							,	metadata={
            """help""": (
                """Will use the token generated when running `huggingface-cli login` (necessary to use this script """
                """with private models)."""
            )
        }							,	)
    __UpperCAmelCase           = field(
        default=a_							,	metadata={"""help""": """Will enable to load a pretrained model whose head dimensions are different."""}							,	)


def 							A						(				__snake_case: Dict  )    ->		Any:

       """simple docstring"""





       __magic_name__      				=    torch.stack([example['pixel_values'] for example in examples]  )
       __magic_name__      				=    torch.tensor([example['labels'] for example in examples]  )
       return {"pixel_values": pixel_values, "labels": labels}


def 							A						(				)    ->		List[str]:

       """simple docstring"""





       __magic_name__      				=    HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)  )
       if len(sys.argv  ) == 2 and sys.argv[1].endswith('.json'  ):
              # If we pass only one argument to the script and it's the path to a json file,
              # let's parse it to get our arguments.
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]  )  )
       else:
              __magic_name__				,  __magic_name__				,  __magic_name__      				=    parser.parse_args_into_dataclasses()

       # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
       # information sent is the one passed as arguments along with your Python/PyTorch versions.
       send_example_telemetry('run_image_classification' ,     __snake_case ,     __snake_case  )

       # Setup logging
       logging.basicConfig(
           format='%(asctime)s - %(levelname)s - %(name)s - %(message)s' ,     datefmt='%m/%d/%Y %H:%M:%S' ,     handlers=[logging.StreamHandler(sys.stdout  )] ,     )

       if training_args.should_log:
              # The default of training_args.log_level is passive, so we set log level at info here to have that default.
              transformers.utils.logging.set_verbosity_info()

       __magic_name__      				=    training_args.get_process_log_level()
       logger.setLevel(__snake_case  )
       transformers.utils.logging.set_verbosity(__snake_case  )
       transformers.utils.logging.enable_default_handler()
       transformers.utils.logging.enable_explicit_format()

       # Log on each process the small summary:
       logger.warning(
           F"""Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"""
           + F"""distributed training: {bool(training_args.local_rank != -1  )}, 16-bits training: {training_args.fpaa}"""  )
       logger.info(F"""Training/evaluation parameters {training_args}"""  )

       # Detecting last checkpoint.
       __magic_name__      				=    None
       if os.path.isdir(training_args.output_dir  ) and training_args.do_train and not training_args.overwrite_output_dir:
              __magic_name__      				=    get_last_checkpoint(training_args.output_dir  )
              if last_checkpoint is None and len(os.listdir(training_args.output_dir  )  ) > 0:
                     raise ValueError(
                         F"""Output directory ({training_args.output_dir}) already exists and is not empty. """
                         'Use --overwrite_output_dir to overcome.'  )
              elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
                     logger.info(
                         F"""Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change """
                         'the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'  )

    # Set seed before initializing model.
       set_seed(training_args.seed  )

       # Initialize our dataset and prepare it for the 'image-classification' task.
       if data_args.dataset_name is not None:
              __magic_name__      				=    load_dataset(
                  data_args.dataset_name ,     data_args.dataset_config_name ,     cache_dir=model_args.cache_dir ,     task='image-classification' ,     use_auth_token=True if model_args.use_auth_token else None ,     )
       else:
              __magic_name__      				=    {}
              if data_args.train_dir is not None:
                     __magic_name__      				=    os.path.join(data_args.train_dir ,     '**'  )
              if data_args.validation_dir is not None:
                     __magic_name__      				=    os.path.join(data_args.validation_dir ,     '**'  )
              __magic_name__      				=    load_dataset(
                  'imagefolder' ,     data_files=__snake_case ,     cache_dir=model_args.cache_dir ,     task='image-classification' ,     )

       # If we don't have a validation split, split off a percentage of train as validation.
       __magic_name__      				=    None if 'validation' in dataset.keys() else data_args.train_val_split
       if isinstance(data_args.train_val_split ,     __snake_case  ) and data_args.train_val_split > 0.0:
              __magic_name__      				=    dataset['train'].train_test_split(data_args.train_val_split  )
              __magic_name__      				=    split['train']
              __magic_name__      				=    split['test']

       # Prepare label mappings.
       # We'll include these in the model's config to get human readable labels in the Inference API.
       __magic_name__      				=    dataset['train'].features['labels'].names
       __magic_name__				,  __magic_name__      				=    {}, {}
       for i, label in enumerate(__snake_case  ):
              __magic_name__      				=    str(__snake_case  )
              __magic_name__      				=    label

       # Load the accuracy metric from the datasets package
       __magic_name__      				=    evaluate.load('accuracy'  )

       # Define our compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
       # predictions and label_ids field) and has to return a dictionary string to float.
       def compute_metrics(__snake_case: str  ):
              return metric.compute(predictions=np.argmax(p.predictions ,     axis=1  ) ,     references=p.label_ids  )

       __magic_name__      				=    AutoConfig.from_pretrained(
           model_args.config_name or model_args.model_name_or_path ,     num_labels=len(__snake_case  ) ,     labelaid=__snake_case ,     idalabel=__snake_case ,     finetuning_task='image-classification' ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     )
       __magic_name__      				=    AutoModelForImageClassification.from_pretrained(
           model_args.model_name_or_path ,     from_tf=bool('.ckpt' in model_args.model_name_or_path  ) ,     config=__snake_case ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     ignore_mismatched_sizes=model_args.ignore_mismatched_sizes ,     )
       __magic_name__      				=    AutoImageProcessor.from_pretrained(
           model_args.image_processor_name or model_args.model_name_or_path ,     cache_dir=model_args.cache_dir ,     revision=model_args.model_revision ,     use_auth_token=True if model_args.use_auth_token else None ,     )

       # Define torchvision transforms to be applied to each image.
       if "shortest_edge" in image_processor.size:
              __magic_name__      				=    image_processor.size['shortest_edge']
       else:
              __magic_name__      				=    (image_processor.size['height'], image_processor.size['width'])
       __magic_name__      				=    Normalize(mean=image_processor.image_mean ,     std=image_processor.image_std  )
       __magic_name__      				=    Compose(
           [
               RandomResizedCrop(__snake_case  ),
               RandomHorizontalFlip(),
               ToTensor(),
               normalize,
           ]  )
       __magic_name__      				=    Compose(
           [
               Resize(__snake_case  ),
               CenterCrop(__snake_case  ),
               ToTensor(),
               normalize,
           ]  )

       def train_transforms(__snake_case: str  ):
              __magic_name__      				=    [
                  _train_transforms(pil_img.convert('RGB'  )  ) for pil_img in example_batch['image']
              ]
              return example_batch

       def val_transforms(__snake_case: List[Any]  ):
              __magic_name__      				=    [_val_transforms(pil_img.convert('RGB'  )  ) for pil_img in example_batch['image']]
              return example_batch

       if training_args.do_train:
              if "train" not in dataset:
                     raise ValueError('--do_train requires a train dataset'  )
              if data_args.max_train_samples is not None:
                     __magic_name__      				=    (
                         dataset['train'].shuffle(seed=training_args.seed  ).select(range(data_args.max_train_samples  )  )
                     )
              # Set the training transforms
              dataset["train"].set_transform(__snake_case  )

       if training_args.do_eval:
              if "validation" not in dataset:
                     raise ValueError('--do_eval requires a validation dataset'  )
              if data_args.max_eval_samples is not None:
                     __magic_name__      				=    (
                         dataset['validation'].shuffle(seed=training_args.seed  ).select(range(data_args.max_eval_samples  )  )
                     )
              # Set the validation transforms
              dataset["validation"].set_transform(__snake_case  )

       # Initalize our trainer
       __magic_name__      				=    Trainer(
           model=__snake_case ,     args=__snake_case ,     train_dataset=dataset['train'] if training_args.do_train else None ,     eval_dataset=dataset['validation'] if training_args.do_eval else None ,     compute_metrics=__snake_case ,     tokenizer=__snake_case ,     data_collator=__snake_case ,     )

       # Training
       if training_args.do_train:
              __magic_name__      				=    None
              if training_args.resume_from_checkpoint is not None:
                     __magic_name__      				=    training_args.resume_from_checkpoint
              elif last_checkpoint is not None:
                     __magic_name__      				=    last_checkpoint
              __magic_name__      				=    trainer.train(resume_from_checkpoint=__snake_case  )
              trainer.save_model()
              trainer.log_metrics('train' ,     train_result.metrics  )
              trainer.save_metrics('train' ,     train_result.metrics  )
              trainer.save_state()

       # Evaluation
       if training_args.do_eval:
              __magic_name__      				=    trainer.evaluate()
              trainer.log_metrics('eval' ,     __snake_case  )
              trainer.save_metrics('eval' ,     __snake_case  )

       # Write model card and (optionally) push to hub
       __magic_name__      				=    {
           'finetuned_from': model_args.model_name_or_path,
           'tasks': 'image-classification',
           'dataset': data_args.dataset_name,
           'tags': ['image-classification', 'vision'],
       }
       if training_args.push_to_hub:
              trainer.push_to_hub(**__snake_case  )
       else:
              trainer.create_model_card(**__snake_case  )


if __name__ == "__main__":
 main()